If there was one overall theme, it would be persuasiveness. In fact, this was presented as self-evident — an almost inherent quality of any great infographic — so the interview primarily focused on what makes an infographic persuasive.
“First, I’d say, they all have a clear focus. The designer has gone in and removed all the extraneous details so you see just what you need to understand the message behind it.”
I couldn’t agree more. In my own graphics, I am constantly trying to simplify and boil them down to the essential elements — from the text and layout to the colors and icons — that help make the point of the graphic clear.
But in the process of simplifying my graphics, I have sometimes found myself approaching a line — and it’s one that you do not want to cross — after which the graphic is too simple, lacks sufficient context, and loses all its weight. For example, I’ve simplified the pie chart below and used color to help emphasize the point of the graphic.
When you’re creating content — whether it’s a film, a sales presentation, or an article on maximizing your Thanksgiving leftovers — it’s always important to consider who your audience is; this also holds true for data visualization. I’ve touched upon this in my previous blog posts, but let‘s take a closer look at the audience spectrum specific to data visualization.
In my previous post, I covered the increasing popularity of "infographics" — both the term and the wide range of examples. I cautioned against unthinking imitation; like most trendy things, their surface shine can distract from their bad qualities, and it’s easy to lose sight of basic principles and objectives. And this distraction is partly to blame for the currently polarized perception of bar charts, which are seen as both antiquated and ideal.
Both Forrester clients and internal colleagues often tell me “We want something better than bar charts” when describing how they would like to see their data visualized. At the same time, I also hear from others, jaded by the onslaught of overdesigned data graphics, who insist there is nothing better or more accurate than bar charts when it comes to visualizing and comparing data points. They don’t need all the “bells and whistles.” “Edward Tufte!” they cry.
So, what’s causing this divide? How can a chart type be so polarizing? I think the answer lies in both the implied perception of bar charts as this basic, limited chart and the array of bad examples of both alternative visualization methods and bar charts themselves.
As the newest blogger for the Data Insights blog, please allow me to introduce myself. My name is Ryan Morrill, and I am a senior data visualization specialist at Forrester. In that role, I’m responsible for creating insightful and engaging graphical stories by exploring the most effective ways to visually represent data and information. I’m really looking forward to sharing my thoughts and lessons learned about data visualization through this blog.
Infographics are popular —or at least the idea of them is popular — and everyone wants to know if, how, and when they should jump on board. Most of the questions I receive from Forrester clients about data visualization relate to "infographics": Should we be using them? How effective are they? What are infographics exactly? How do we make them ourselves?
In a recent media interview I was asked about whether the requirements for data visualization had changed. The questions were focused around whether users are still satisfied with dashboards, graphs and charts or do they have new needs, demands and expectations.
Arguably, Ancient Egyptian hieroglyphics were probably the first real "commercial" examples of data visualization (though many people before the Egyptians also used the same approach — but more often as a general communications tool). Since then, visualization of data has certainly always been both a popular and important topic. For example, Florence Nightingale changed the course of healthcare with a single compelling polar area chart on the causes of death during the Crimean War.
In looking at this question of how and why data visualization might be changing, I identified at least 5 major triggers. Namely:
Increasing volumes of data. It's no surprise that we now have to process much larger volumes of data. But this also impacts the ways we need to represent it. The volume of data stimulates new forms of visualization tools. While not all of these tools are new (strictly speaking), they have at least begun to find a much broader audience as we find the need to communicate much more information much more rapidly. Time walling and infographics are just two approaches that are not necessarily all that new but they have attracted much greater usage as a direct result of the increasing volume of data.
As one of the industry-renowned data visualization experts Edward Tufte once said, “The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?” Indeed, there’s just too much information out there for all categories of knowledge workers to visualize it effectively. More often than not, traditional reports using tabs, rows, and columns do not paint the whole picture or, even worse, lead an analyst to a wrong conclusion. Firms need to use data visualization because information workers:
Cannot see a pattern without data visualization. Simply seeing numbers on a grid often does not convey the whole story — and in the worst case, it can even lead to a wrong conclusion. This is best demonstrated by Anscombe’s quartet where four seemingly similar groups of x/y coordinates reveal very different patterns when represented in a graph.
Cannot fit all of the necessary data points onto a single screen. Even with the smallest reasonably readable font, single-line spacing, and no grid, one cannot realistically fit more than a few thousand data points on a single page or screen using numerical information only. When using advanced data visualization techniques, one can fit tens of thousands (an order-of-magnitude difference) of data points onto a single screen. In his book The Visual Display of Quantitative Information, Edward Tufte gives an example of more than 21,000 data points effectively displayed on a US map that fits onto a single screen.
As one of the industry-renowned data visualization experts Edward Tufte once said, “The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?” There’s indeed just too much information out there to be effectively analyzed by all categories of knowledge workers. More often than not, traditional tabular row-and-column reports do not paint the whole picture or — even worse — can lead an analyst to a wrong conclusion. There are multiple reasons to use data visualization; the three main ones are that one:
Cannot see a pattern without data visualization. Simply seeing numbers on a grid often does not tell the whole story; in the worst case, it can even lead one to a wrong conclusion. This is best demonstrated by Anscombe’s quartet, where four seemingly similar groups of x and y coordinates reveal very different patterns when represented in a graph.
Cannot fit all of the necessary data points onto a single screen. Even with the smallest reasonably readable font, single line spacing, and no grid, one cannot realistically fit more than a few thousand data points using numerical information only. When using advanced data visualization techniques, one can fit tens of thousands data points onto a single screen — a difference of an order of magnitude. In The Visual Display of Quantitative Information, Edward Tufte gives an example of more than 21,000 data points effectively displayed on a US map that fits onto a single screen.